Overview

Dataset statistics

Number of variables55
Number of observations581012
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory243.8 MiB
Average record size in memory440.0 B

Variable types

Numeric11
Categorical44

Alerts

Elevation is highly overall correlated with Wilderness_Area4 and 2 other fieldsHigh correlation
Aspect is highly overall correlated with Hillshade_3pmHigh correlation
Horizontal_Distance_To_Hydrology is highly overall correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly overall correlated with Horizontal_Distance_To_HydrologyHigh correlation
Hillshade_9am is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_3pm is highly overall correlated with Aspect and 2 other fieldsHigh correlation
target is highly overall correlated with Wilderness_Area4High correlation
Wilderness_Area1 is highly overall correlated with Wilderness_Area3 and 1 other fieldsHigh correlation
Wilderness_Area3 is highly overall correlated with Wilderness_Area1High correlation
Wilderness_Area4 is highly overall correlated with Elevation and 1 other fieldsHigh correlation
Soil_Type10 is highly overall correlated with ElevationHigh correlation
Soil_Type29 is highly overall correlated with Wilderness_Area1High correlation
Soil_Type40 is highly overall correlated with ElevationHigh correlation
Wilderness_Area2 is highly imbalanced (70.8%)Imbalance
Wilderness_Area4 is highly imbalanced (65.8%)Imbalance
Soil_Type1 is highly imbalanced (95.3%)Imbalance
Soil_Type2 is highly imbalanced (90.0%)Imbalance
Soil_Type3 is highly imbalanced (93.1%)Imbalance
Soil_Type4 is highly imbalanced (85.1%)Imbalance
Soil_Type5 is highly imbalanced (97.3%)Imbalance
Soil_Type6 is highly imbalanced (91.1%)Imbalance
Soil_Type7 is highly imbalanced (99.7%)Imbalance
Soil_Type8 is highly imbalanced (99.6%)Imbalance
Soil_Type9 is highly imbalanced (97.9%)Imbalance
Soil_Type10 is highly imbalanced (68.8%)Imbalance
Soil_Type11 is highly imbalanced (85.1%)Imbalance
Soil_Type12 is highly imbalanced (70.7%)Imbalance
Soil_Type13 is highly imbalanced (80.6%)Imbalance
Soil_Type14 is highly imbalanced (98.8%)Imbalance
Soil_Type15 is highly imbalanced (> 99.9%)Imbalance
Soil_Type16 is highly imbalanced (95.5%)Imbalance
Soil_Type17 is highly imbalanced (94.8%)Imbalance
Soil_Type18 is highly imbalanced (96.8%)Imbalance
Soil_Type19 is highly imbalanced (94.0%)Imbalance
Soil_Type20 is highly imbalanced (88.2%)Imbalance
Soil_Type21 is highly imbalanced (98.4%)Imbalance
Soil_Type22 is highly imbalanced (68.3%)Imbalance
Soil_Type23 is highly imbalanced (53.3%)Imbalance
Soil_Type24 is highly imbalanced (77.3%)Imbalance
Soil_Type25 is highly imbalanced (99.0%)Imbalance
Soil_Type26 is highly imbalanced (95.9%)Imbalance
Soil_Type27 is highly imbalanced (98.0%)Imbalance
Soil_Type28 is highly imbalanced (98.3%)Imbalance
Soil_Type30 is highly imbalanced (70.5%)Imbalance
Soil_Type31 is highly imbalanced (73.9%)Imbalance
Soil_Type32 is highly imbalanced (56.2%)Imbalance
Soil_Type33 is highly imbalanced (60.6%)Imbalance
Soil_Type34 is highly imbalanced (97.2%)Imbalance
Soil_Type35 is highly imbalanced (96.8%)Imbalance
Soil_Type36 is highly imbalanced (99.7%)Imbalance
Soil_Type37 is highly imbalanced (99.4%)Imbalance
Soil_Type38 is highly imbalanced (82.2%)Imbalance
Soil_Type39 is highly imbalanced (83.8%)Imbalance
Soil_Type40 is highly imbalanced (88.7%)Imbalance
Horizontal_Distance_To_Hydrology has 24603 (4.2%) zerosZeros
Vertical_Distance_To_Hydrology has 38665 (6.7%) zerosZeros

Reproduction

Analysis started2023-02-02 17:32:13.032826
Analysis finished2023-02-02 17:34:28.568105
Duration2 minutes and 15.54 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

Distinct1978
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2959.3653
Minimum1859
Maximum3858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2023-02-02T18:34:28.656014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1859
5-th percentile2406
Q12809
median2996
Q33163
95-th percentile3336
Maximum3858
Range1999
Interquartile range (IQR)354

Descriptive statistics

Standard deviation279.98473
Coefficient of variation (CV)0.094609724
Kurtosis0.74925078
Mean2959.3653
Median Absolute Deviation (MAD)175
Skewness-0.81759582
Sum1.7194268 × 109
Variance78391.451
MonotonicityNot monotonic
2023-02-02T18:34:28.791307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2968 1681
 
0.3%
2962 1674
 
0.3%
2991 1671
 
0.3%
2972 1662
 
0.3%
2975 1656
 
0.3%
2978 1656
 
0.3%
2988 1619
 
0.3%
2955 1590
 
0.3%
2952 1577
 
0.3%
2965 1571
 
0.3%
Other values (1968) 564655
97.2%
ValueCountFrequency (%)
1859 1
 
< 0.1%
1860 1
 
< 0.1%
1861 1
 
< 0.1%
1863 1
 
< 0.1%
1866 1
 
< 0.1%
1867 1
 
< 0.1%
1868 1
 
< 0.1%
1871 3
< 0.1%
1872 4
< 0.1%
1873 1
 
< 0.1%
ValueCountFrequency (%)
3858 2
 
< 0.1%
3857 1
 
< 0.1%
3856 1
 
< 0.1%
3853 1
 
< 0.1%
3852 1
 
< 0.1%
3851 2
 
< 0.1%
3850 1
 
< 0.1%
3849 4
< 0.1%
3848 1
 
< 0.1%
3846 6
< 0.1%

Aspect
Real number (ℝ)

Distinct361
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.65681
Minimum0
Maximum360
Zeros4914
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2023-02-02T18:34:28.932597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q158
median127
Q3260
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)202

Descriptive statistics

Standard deviation111.91372
Coefficient of variation (CV)0.71897736
Kurtosis-1.2202389
Mean155.65681
Median Absolute Deviation (MAD)85
Skewness0.40262832
Sum90438473
Variance12524.681
MonotonicityNot monotonic
2023-02-02T18:34:29.061267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 6308
 
1.1%
0 4914
 
0.8%
90 4677
 
0.8%
135 3834
 
0.7%
63 3680
 
0.6%
315 3574
 
0.6%
72 3407
 
0.6%
18 3403
 
0.6%
27 3392
 
0.6%
34 2836
 
0.5%
Other values (351) 540987
93.1%
ValueCountFrequency (%)
0 4914
0.8%
1 1671
 
0.3%
2 1902
 
0.3%
3 1945
 
0.3%
4 2267
0.4%
5 2063
0.4%
6 2242
0.4%
7 2194
0.4%
8 2213
0.4%
9 2460
0.4%
ValueCountFrequency (%)
360 51
 
< 0.1%
359 1407
0.2%
358 1749
0.3%
357 1860
0.3%
356 2025
0.3%
355 1933
0.3%
354 2025
0.3%
353 1946
0.3%
352 1985
0.3%
351 2184
0.4%

Slope
Real number (ℝ)

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.103704
Minimum0
Maximum66
Zeros656
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2023-02-02T18:34:29.192140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median13
Q318
95-th percentile28
Maximum66
Range66
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.4882418
Coefficient of variation (CV)0.53094152
Kurtosis0.58119911
Mean14.103704
Median Absolute Deviation (MAD)5
Skewness0.78927255
Sum8194421
Variance56.073765
MonotonicityNot monotonic
2023-02-02T18:34:29.316945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 33824
 
5.8%
10 33812
 
5.8%
12 33217
 
5.7%
13 32419
 
5.6%
9 32049
 
5.5%
14 30282
 
5.2%
8 30130
 
5.2%
15 29127
 
5.0%
16 26541
 
4.6%
7 26395
 
4.5%
Other values (57) 273216
47.0%
ValueCountFrequency (%)
0 656
 
0.1%
1 3680
 
0.6%
2 7726
 
1.3%
3 11620
 
2.0%
4 16344
2.8%
5 20810
3.6%
6 24504
4.2%
7 26395
4.5%
8 30130
5.2%
9 32049
5.5%
ValueCountFrequency (%)
66 1
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
63 1
 
< 0.1%
62 2
 
< 0.1%
61 4
< 0.1%
60 2
 
< 0.1%
59 3
< 0.1%
58 1
 
< 0.1%
57 7
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.42822
Minimum0
Maximum1397
Zeros24603
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2023-02-02T18:34:29.463822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median218
Q3384
95-th percentile684
Maximum1397
Range1397
Interquartile range (IQR)276

Descriptive statistics

Standard deviation212.54936
Coefficient of variation (CV)0.78889048
Kurtosis1.3661805
Mean269.42822
Median Absolute Deviation (MAD)133
Skewness1.1404374
Sum1.5654103 × 108
Variance45177.229
MonotonicityNot monotonic
2023-02-02T18:34:29.600583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 34139
 
5.9%
0 24603
 
4.2%
150 20785
 
3.6%
60 19189
 
3.3%
67 15223
 
2.6%
42 14647
 
2.5%
108 14358
 
2.5%
85 13741
 
2.4%
90 11140
 
1.9%
120 10673
 
1.8%
Other values (541) 402514
69.3%
ValueCountFrequency (%)
0 24603
4.2%
30 34139
5.9%
42 14647
2.5%
60 19189
3.3%
67 15223
2.6%
85 13741
2.4%
90 11140
 
1.9%
95 9216
 
1.6%
108 14358
2.5%
120 10673
 
1.8%
ValueCountFrequency (%)
1397 1
< 0.1%
1390 2
< 0.1%
1383 2
< 0.1%
1382 1
< 0.1%
1376 1
< 0.1%
1371 1
< 0.1%
1370 1
< 0.1%
1369 1
< 0.1%
1368 2
< 0.1%
1361 2
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct700
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.418855
Minimum-173
Maximum601
Zeros38665
Zeros (%)6.7%
Negative55143
Negative (%)9.5%
Memory size4.4 MiB
2023-02-02T18:34:29.745942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-173
5-th percentile-8
Q17
median30
Q369
95-th percentile165
Maximum601
Range774
Interquartile range (IQR)62

Descriptive statistics

Standard deviation58.295232
Coefficient of variation (CV)1.2558524
Kurtosis5.2502958
Mean46.418855
Median Absolute Deviation (MAD)27
Skewness1.7902497
Sum26969912
Variance3398.334
MonotonicityNot monotonic
2023-02-02T18:34:29.880140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38665
 
6.7%
3 9298
 
1.6%
10 8863
 
1.5%
7 8741
 
1.5%
6 8590
 
1.5%
13 8474
 
1.5%
4 8397
 
1.4%
5 7614
 
1.3%
16 7429
 
1.3%
9 7331
 
1.3%
Other values (690) 467610
80.5%
ValueCountFrequency (%)
-173 1
 
< 0.1%
-166 2
< 0.1%
-164 1
 
< 0.1%
-163 1
 
< 0.1%
-161 1
 
< 0.1%
-159 3
< 0.1%
-158 1
 
< 0.1%
-157 2
< 0.1%
-156 2
< 0.1%
-155 3
< 0.1%
ValueCountFrequency (%)
601 1
 
< 0.1%
599 1
 
< 0.1%
598 2
< 0.1%
597 3
< 0.1%
595 2
< 0.1%
592 1
 
< 0.1%
591 1
 
< 0.1%
590 2
< 0.1%
589 3
< 0.1%
588 3
< 0.1%
Distinct5785
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2350.1466
Minimum0
Maximum7117
Zeros124
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2023-02-02T18:34:30.014542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile379
Q11106
median1997
Q33328
95-th percentile5483
Maximum7117
Range7117
Interquartile range (IQR)2222

Descriptive statistics

Standard deviation1559.2549
Coefficient of variation (CV)0.66347132
Kurtosis-0.38371119
Mean2350.1466
Median Absolute Deviation (MAD)1040
Skewness0.71367882
Sum1.3654634 × 109
Variance2431275.7
MonotonicityNot monotonic
2023-02-02T18:34:30.133833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1332
 
0.2%
618 1065
 
0.2%
900 918
 
0.2%
390 914
 
0.2%
1020 900
 
0.2%
990 878
 
0.2%
960 868
 
0.1%
997 859
 
0.1%
750 847
 
0.1%
1140 840
 
0.1%
Other values (5775) 571591
98.4%
ValueCountFrequency (%)
0 124
 
< 0.1%
30 313
0.1%
42 171
 
< 0.1%
60 312
0.1%
67 298
0.1%
85 384
0.1%
90 380
0.1%
95 374
0.1%
108 660
0.1%
120 633
0.1%
ValueCountFrequency (%)
7117 1
< 0.1%
7116 1
< 0.1%
7112 1
< 0.1%
7097 1
< 0.1%
7092 1
< 0.1%
7087 2
< 0.1%
7082 1
< 0.1%
7079 1
< 0.1%
7078 2
< 0.1%
7069 1
< 0.1%

Hillshade_9am
Real number (ℝ)

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.14605
Minimum0
Maximum254
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2023-02-02T18:34:30.261240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile160
Q1198
median218
Q3231
95-th percentile246
Maximum254
Range254
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.769889
Coefficient of variation (CV)0.12618613
Kurtosis1.8755177
Mean212.14605
Median Absolute Deviation (MAD)16
Skewness-1.1811467
Sum1.232594 × 108
Variance716.62695
MonotonicityNot monotonic
2023-02-02T18:34:30.385071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 11657
 
2.0%
228 11374
 
2.0%
230 11355
 
2.0%
224 11210
 
1.9%
223 10887
 
1.9%
222 10809
 
1.9%
233 10645
 
1.8%
227 10513
 
1.8%
225 10307
 
1.8%
221 10264
 
1.8%
Other values (197) 471991
81.2%
ValueCountFrequency (%)
0 13
< 0.1%
36 1
 
< 0.1%
46 2
 
< 0.1%
50 1
 
< 0.1%
52 2
 
< 0.1%
53 1
 
< 0.1%
54 4
 
< 0.1%
55 1
 
< 0.1%
56 6
< 0.1%
57 2
 
< 0.1%
ValueCountFrequency (%)
254 1898
 
0.3%
253 2236
0.4%
252 2563
0.4%
251 2968
0.5%
250 3341
0.6%
249 3793
0.7%
248 3955
0.7%
247 4443
0.8%
246 5008
0.9%
245 5530
1.0%

Hillshade_Noon
Real number (ℝ)

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.31872
Minimum0
Maximum254
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2023-02-02T18:34:30.505617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186
Q1213
median226
Q3237
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.768697
Coefficient of variation (CV)0.088522348
Kurtosis2.0662108
Mean223.31872
Median Absolute Deviation (MAD)12
Skewness-1.0630563
Sum1.2975085 × 108
Variance390.80139
MonotonicityNot monotonic
2023-02-02T18:34:30.743899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 13696
 
2.4%
231 13666
 
2.4%
233 13297
 
2.3%
229 13271
 
2.3%
230 13258
 
2.3%
234 13047
 
2.2%
227 13020
 
2.2%
223 12989
 
2.2%
226 12953
 
2.2%
225 12928
 
2.2%
Other values (175) 448887
77.3%
ValueCountFrequency (%)
0 5
< 0.1%
30 1
 
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
45 1
 
< 0.1%
53 2
 
< 0.1%
63 1
 
< 0.1%
64 1
 
< 0.1%
68 1
 
< 0.1%
71 1
 
< 0.1%
ValueCountFrequency (%)
254 5902
1.0%
253 6300
1.1%
252 7171
1.2%
251 7471
1.3%
250 8028
1.4%
249 7714
1.3%
248 8133
1.4%
247 8874
1.5%
246 8665
1.5%
245 8538
1.5%

Hillshade_3pm
Real number (ℝ)

Distinct255
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.52826
Minimum0
Maximum254
Zeros1338
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2023-02-02T18:34:30.864928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78
Q1119
median143
Q3168
95-th percentile204
Maximum254
Range254
Interquartile range (IQR)49

Descriptive statistics

Standard deviation38.274529
Coefficient of variation (CV)0.26853993
Kurtosis0.39844001
Mean142.52826
Median Absolute Deviation (MAD)25
Skewness-0.2770532
Sum82810631
Variance1464.9396
MonotonicityNot monotonic
2023-02-02T18:34:30.991806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 7333
 
1.3%
145 7217
 
1.2%
138 7065
 
1.2%
146 6915
 
1.2%
142 6902
 
1.2%
136 6871
 
1.2%
139 6858
 
1.2%
135 6781
 
1.2%
149 6723
 
1.2%
132 6673
 
1.1%
Other values (245) 511674
88.1%
ValueCountFrequency (%)
0 1338
0.2%
1 15
 
< 0.1%
2 15
 
< 0.1%
3 15
 
< 0.1%
4 20
 
< 0.1%
5 18
 
< 0.1%
6 26
 
< 0.1%
7 30
 
< 0.1%
8 21
 
< 0.1%
9 33
 
< 0.1%
ValueCountFrequency (%)
254 4
 
< 0.1%
253 8
 
< 0.1%
252 16
 
< 0.1%
251 11
 
< 0.1%
250 17
 
< 0.1%
249 37
< 0.1%
248 44
< 0.1%
247 61
< 0.1%
246 72
< 0.1%
245 85
< 0.1%
Distinct5827
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1980.2912
Minimum0
Maximum7173
Zeros51
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2023-02-02T18:34:31.116365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile418
Q11024
median1710
Q32550
95-th percentile4944
Maximum7173
Range7173
Interquartile range (IQR)1526

Descriptive statistics

Standard deviation1324.1952
Coefficient of variation (CV)0.66868711
Kurtosis1.6458068
Mean1980.2912
Median Absolute Deviation (MAD)750
Skewness1.2886441
Sum1.150573 × 109
Variance1753493
MonotonicityNot monotonic
2023-02-02T18:34:31.236165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618 1412
 
0.2%
541 1099
 
0.2%
607 1054
 
0.2%
942 1023
 
0.2%
997 1004
 
0.2%
700 958
 
0.2%
900 937
 
0.2%
726 923
 
0.2%
752 910
 
0.2%
960 908
 
0.2%
Other values (5817) 570784
98.2%
ValueCountFrequency (%)
0 51
 
< 0.1%
30 206
< 0.1%
42 207
< 0.1%
60 206
< 0.1%
67 416
0.1%
85 207
< 0.1%
90 204
< 0.1%
95 412
0.1%
108 412
0.1%
120 204
< 0.1%
ValueCountFrequency (%)
7173 1
< 0.1%
7172 1
< 0.1%
7168 1
< 0.1%
7150 1
< 0.1%
7145 1
< 0.1%
7142 1
< 0.1%
7141 2
< 0.1%
7140 1
< 0.1%
7131 1
< 0.1%
7126 1
< 0.1%

Wilderness_Area1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
320216 
1
260796 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Length

2023-02-02T18:34:31.347737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:31.446339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Most occurring characters

ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Wilderness_Area2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
551128 
1
 
29884

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Length

2023-02-02T18:34:31.522771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:31.612430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Wilderness_Area3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
327648 
1
253364 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Length

2023-02-02T18:34:31.684849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:31.776836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Most occurring characters

ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Wilderness_Area4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
544044 
1
 
36968

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Length

2023-02-02T18:34:31.852521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:31.943967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Soil_Type1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
577981 
1
 
3031

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 577981
99.5%
1 3031
 
0.5%

Length

2023-02-02T18:34:32.017185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:32.111300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 577981
99.5%
1 3031
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 577981
99.5%
1 3031
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 577981
99.5%
1 3031
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 577981
99.5%
1 3031
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 577981
99.5%
1 3031
 
0.5%

Soil_Type2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
573487 
1
 
7525

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Length

2023-02-02T18:34:32.185084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:32.276573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Soil_Type3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
576189 
1
 
4823

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Length

2023-02-02T18:34:32.349972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:32.441643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Soil_Type4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
568616 
1
 
12396

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Length

2023-02-02T18:34:32.514746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:32.605323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Soil_Type5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579415 
1
 
1597

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Length

2023-02-02T18:34:32.679686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:32.771175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Soil_Type6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
574437 
1
 
6575

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Length

2023-02-02T18:34:32.846006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:32.937491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Soil_Type7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580907 
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Length

2023-02-02T18:34:33.011542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:33.101727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Soil_Type8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580833 
1
 
179

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Length

2023-02-02T18:34:33.175472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:33.265458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Soil_Type9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579865 
1
 
1147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Length

2023-02-02T18:34:33.338502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:33.430727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Soil_Type10
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
548378 
1
 
32634

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Length

2023-02-02T18:34:33.504322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:33.596291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Soil_Type11
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
568602 
1
 
12410

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Length

2023-02-02T18:34:33.669547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:33.761025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Soil_Type12
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
551041 
1
 
29971

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Length

2023-02-02T18:34:33.835416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:33.927058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Soil_Type13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
563581 
1
 
17431

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Length

2023-02-02T18:34:34.002270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:34.093530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Soil_Type14
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580413 
1
 
599

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Length

2023-02-02T18:34:34.168315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:34.258904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Soil_Type15
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
581009 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Length

2023-02-02T18:34:34.333363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:34.423689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Soil_Type16
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
578167 
1
 
2845

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Length

2023-02-02T18:34:34.497967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:34.589356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Soil_Type17
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
577590 
1
 
3422

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Length

2023-02-02T18:34:34.662663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:34.755645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Soil_Type18
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579113 
1
 
1899

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Length

2023-02-02T18:34:34.830839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:34.926125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Soil_Type19
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
576991 
1
 
4021

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Length

2023-02-02T18:34:34.999272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:35.090743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Soil_Type20
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
571753 
1
 
9259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Length

2023-02-02T18:34:35.164196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:35.254350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Soil_Type21
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580174 
1
 
838

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Length

2023-02-02T18:34:35.434653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:35.523951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Soil_Type22
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
547639 
1
 
33373

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Length

2023-02-02T18:34:35.597759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:35.688255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Soil_Type23
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
523260 
1
57752 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Length

2023-02-02T18:34:35.762204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:35.853312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Soil_Type24
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
559734 
1
 
21278

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Length

2023-02-02T18:34:35.929402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:36.020238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Soil_Type25
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580538 
1
 
474

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Length

2023-02-02T18:34:36.094368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:36.184920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Soil_Type26
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
578423 
1
 
2589

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Length

2023-02-02T18:34:36.259323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:36.350320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Soil_Type27
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579926 
1
 
1086

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Length

2023-02-02T18:34:36.423641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:36.515841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Soil_Type28
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580066 
1
 
946

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Length

2023-02-02T18:34:36.589340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:36.681527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Soil_Type29
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
465765 
1
115247 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Length

2023-02-02T18:34:36.768854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:36.863096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Soil_Type30
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
550842 
1
 
30170

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Length

2023-02-02T18:34:36.938745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:37.030106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Soil_Type31
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
555346 
1
 
25666

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Length

2023-02-02T18:34:37.103415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:37.195342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Soil_Type32
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
528493 
1
 
52519

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Length

2023-02-02T18:34:37.268516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:37.366110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Soil_Type33
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
535858 
1
 
45154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Length

2023-02-02T18:34:37.443178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:37.534208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Soil_Type34
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579401 
1
 
1611

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Length

2023-02-02T18:34:37.608724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:37.699428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Soil_Type35
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579121 
1
 
1891

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Length

2023-02-02T18:34:37.773916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:37.865609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Soil_Type36
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580893 
1
 
119

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Length

2023-02-02T18:34:37.944423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:38.049141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Soil_Type37
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580714 
1
 
298

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Length

2023-02-02T18:34:38.124219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:38.216212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Soil_Type38
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
565439 
1
 
15573

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Length

2023-02-02T18:34:38.294175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:38.386711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Soil_Type39
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
567206 
1
 
13806

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Length

2023-02-02T18:34:38.461030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:38.556342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Soil_Type40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
572262 
1
 
8750

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

Length

2023-02-02T18:34:38.630413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:34:38.721731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

target
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0514705
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2023-02-02T18:34:38.790615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3965043
Coefficient of variation (CV)0.6807333
Kurtosis4.9489652
Mean2.0514705
Median Absolute Deviation (MAD)1
Skewness2.2765737
Sum1191929
Variance1.9502243
MonotonicityNot monotonic
2023-02-02T18:34:38.865027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 283301
48.8%
1 211840
36.5%
3 35754
 
6.2%
7 20510
 
3.5%
6 17367
 
3.0%
5 9493
 
1.6%
4 2747
 
0.5%
ValueCountFrequency (%)
1 211840
36.5%
2 283301
48.8%
3 35754
 
6.2%
4 2747
 
0.5%
5 9493
 
1.6%
6 17367
 
3.0%
7 20510
 
3.5%
ValueCountFrequency (%)
7 20510
 
3.5%
6 17367
 
3.0%
5 9493
 
1.6%
4 2747
 
0.5%
3 35754
 
6.2%
2 283301
48.8%
1 211840
36.5%

Interactions

2023-02-02T18:34:22.251435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:04.145329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:05.984378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:07.949055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:09.717182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:11.657457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:13.653539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:15.401957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:17.019800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:18.668962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:20.377828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:22.425286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:04.312201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:06.140205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:08.103675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:09.882760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:11.829088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:13.850852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:15.540103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:17.160792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:18.821288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:20.544092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:22.581099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:04.476606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:06.294617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:08.255184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:10.062445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:11.996430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:14.011514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:15.677838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:17.298289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:18.971696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:20.711315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:22.744342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:04.656382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:06.455797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:08.404178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:10.213623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:12.178879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:14.172233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:15.813451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:17.434098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:19.118838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:20.874679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:22.916241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:04.805669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:06.679672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:08.568939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:10.366019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:12.377810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:14.328161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:15.970907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:17.579689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:19.280461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:21.039950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:23.081442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:04.968742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:06.966995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:08.732655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:10.674259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:12.563627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:14.494182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:16.123067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:17.820227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:19.441808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:21.223066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:23.238506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:05.122535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:07.133059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:08.936391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:10.834945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:12.757004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:14.670474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:16.279220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:17.962174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:19.596226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:21.403038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:23.384386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:05.261561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:07.291604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:09.100671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:10.984280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:12.928748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:14.836559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:16.429690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:18.090272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:19.758438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:21.565263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:23.527612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:05.411588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:07.465953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:09.250904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:11.139320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:13.096503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:14.983983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:16.606656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:18.220403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:19.913462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:21.712486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:23.672287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:05.603641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:07.621404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:09.421718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:11.302976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:13.263739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:15.120314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:16.739341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:18.355904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:20.079718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:21.885833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:23.823420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:05.828569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:07.782431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:09.574987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:11.486593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:13.443191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:15.260978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:16.884165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:18.523677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:20.232156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:34:22.061075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-02T18:34:39.040895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointstargetWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40
Elevation1.0000.044-0.1600.2870.0870.3420.0150.1500.0730.155-0.4910.2790.2980.1990.8810.3640.2540.2270.2390.2880.3110.0200.0260.1530.5140.2180.2780.1490.1290.0160.0700.1320.1900.0680.0880.0240.2240.1930.0880.0360.0710.0410.0620.2380.1230.0930.1900.0980.0330.1360.0400.0670.3860.3130.622
Aspect0.0441.0000.0720.0050.0730.019-0.4290.4210.641-0.1130.0250.2090.0710.1910.1300.0480.0640.1060.1770.0260.0260.0070.0040.0310.1590.0780.0940.1730.0130.0040.0190.0180.0460.0110.0450.0480.0420.0280.1590.0590.0740.0670.0690.1110.1320.0920.0950.0660.0610.0300.0370.0270.0320.0460.034
Slope-0.1600.0721.0000.0190.301-0.205-0.131-0.434-0.173-0.1700.1510.2310.0410.1480.2760.1220.0210.1440.1380.0850.0120.0200.0350.0340.2470.0710.1750.2010.0010.0000.0370.0440.0480.1060.0790.0260.0570.2120.1140.0360.0450.0510.0900.0950.0810.0840.1400.2310.0130.0240.0150.0090.0740.0960.031
Horizontal_Distance_To_Hydrology0.2870.0050.0191.0000.6190.047-0.0420.0280.0380.074-0.0280.1090.0680.1360.1030.0350.0470.0430.0500.0160.0410.0190.0100.0250.0780.0230.0760.0190.0460.0000.0760.0920.0200.0560.1020.0480.0570.1670.0380.0270.0190.1110.0330.0720.0540.0750.1390.0990.1050.0360.0670.0170.0700.0430.166
Vertical_Distance_To_Hydrology0.0870.0730.3010.6191.000-0.038-0.129-0.0960.038-0.0430.0960.1850.0630.1450.0940.0230.0300.0350.0350.0340.0470.0080.0110.0280.0670.0300.0710.0870.0220.0000.0470.0490.0390.0510.0660.0240.0760.1620.0410.1390.0220.1170.1230.0900.0240.0440.0560.1660.0750.0200.0220.0050.0200.0540.214
Horizontal_Distance_To_Roadways0.3420.019-0.2050.047-0.0381.000-0.0100.1740.1020.322-0.2220.4830.2260.3990.3570.1320.0930.1010.0950.0990.1410.0540.0470.0620.2040.1070.0990.1200.0450.0040.0550.0590.0730.0920.0890.0370.1330.0500.0680.0730.0780.0500.0710.3310.1210.1450.1580.1360.0460.0510.0330.0470.0910.1110.069
Hillshade_9am0.015-0.429-0.131-0.042-0.129-0.0101.000-0.101-0.8230.1240.0130.2110.0280.1320.2570.0480.0370.0560.0480.0710.0240.0110.0170.0260.2560.0570.1190.1240.0120.0000.0250.0250.0350.0540.0550.0310.0410.1230.1220.0400.0380.0470.1520.0840.1380.0830.0980.1000.0190.0290.0090.0120.0390.0700.017
Hillshade_Noon0.1500.421-0.4340.028-0.0960.174-0.1011.0000.5740.017-0.0340.0990.0490.0670.2200.0900.0420.0360.0780.0790.0170.0060.0140.0130.2500.0740.0810.0840.0000.0010.0180.0300.0220.0490.0360.0320.0390.1310.1410.0100.0330.0240.0080.0800.0430.0510.1210.1290.0380.0150.0160.0240.0460.0720.041
Hillshade_3pm0.0730.641-0.1730.0380.0380.102-0.8230.5741.000-0.083-0.0360.1870.0550.1150.1860.1480.0190.1260.0550.0590.0160.0110.0160.0270.1330.0860.1390.2030.0090.0000.0360.0380.0500.0580.0680.0460.0460.1410.0280.0360.0330.0550.1730.0920.1320.0570.1260.1270.0270.0300.0130.0240.0570.0870.040
Horizontal_Distance_To_Fire_Points0.155-0.113-0.1700.074-0.0430.3220.1240.017-0.0831.000-0.1370.3810.1130.3100.3170.1170.0970.1000.0870.0660.1080.0820.0420.0520.2090.0780.2960.1110.0510.0040.1030.0400.1570.0250.1220.0300.0540.0790.0700.0740.0730.0440.0380.2160.0740.0780.1060.0910.0460.0500.0210.0350.0930.0440.064
target-0.4910.0250.151-0.0280.096-0.2220.013-0.034-0.036-0.1371.0000.3110.1500.1120.7360.2240.3100.3260.3420.1630.2820.0130.0100.0330.4710.1090.1980.1380.1570.0130.0370.1930.0590.0480.0450.0470.2120.1870.0840.0150.0530.0200.0370.1970.1480.0800.1070.0940.0430.1490.0380.1180.3410.3310.266
Wilderness_Area10.2790.2090.2310.1090.1850.4830.2110.0990.1870.3810.3111.0000.2100.7940.2350.0650.1030.0830.1330.0470.0970.0150.0190.0490.2200.1330.2580.1590.0290.0000.0430.0690.0590.0390.0720.0340.0690.0300.1220.0260.0600.0390.0360.5510.2590.1940.2840.2620.0480.0120.0130.0150.0110.0130.012
Wilderness_Area20.2980.0710.0410.0680.0630.2260.0280.0490.0550.1130.1500.2101.0000.2050.0610.0170.0270.0210.0340.0120.0250.0030.0040.0100.0570.0340.0540.0290.0070.0000.0030.0180.0030.0370.0260.0090.1220.1350.0430.1230.0150.0100.0090.1140.0540.0340.0290.0140.0120.0550.0030.0050.0610.0110.105
Wilderness_Area30.1990.1910.1480.1360.1450.3990.1320.0670.1150.3100.1120.7940.2051.0000.2290.0640.0640.0100.1380.0460.0940.0120.0150.0390.0070.1540.2050.1950.0020.0000.0450.0520.0500.0450.0440.0430.0920.0480.1290.0250.0760.0490.0460.4370.2060.2370.3130.2940.0600.0050.0160.0100.0170.0020.043
Wilderness_Area40.8810.1300.2760.1030.0940.3570.2570.2200.1860.3170.7360.2350.0610.2291.0000.2780.1040.1670.0220.2010.4100.0030.0040.0110.4850.0090.0610.0460.0700.0070.0080.0530.0150.0220.0330.0100.0640.0870.0510.0070.0170.0110.0100.1300.0610.0560.0820.0760.0140.0150.0030.0060.0430.0410.032
Soil_Type10.3640.0480.1220.0350.0230.1320.0480.0900.1480.1170.2240.0650.0170.0640.2781.0000.0080.0060.0110.0030.0080.0000.0000.0030.0180.0110.0170.0130.0010.0000.0050.0050.0040.0060.0090.0020.0180.0240.0140.0010.0040.0030.0020.0360.0170.0150.0230.0210.0030.0040.0000.0000.0120.0110.009
Soil_Type20.2540.0640.0210.0470.0300.0930.0370.0420.0190.0970.3100.1030.0270.0640.1040.0081.0000.0100.0170.0060.0120.0000.0010.0050.0280.0170.0270.0200.0030.0000.0080.0090.0060.0090.0140.0040.0280.0380.0220.0030.0070.0050.0040.0570.0270.0250.0360.0330.0060.0060.0000.0020.0190.0180.014
Soil_Type30.2270.1060.1440.0430.0350.1010.0560.0360.1260.1000.3260.0830.0210.0100.1670.0060.0101.0000.0130.0040.0100.0000.0000.0040.0220.0130.0210.0160.0020.0000.0060.0070.0050.0070.0110.0030.0230.0300.0180.0020.0060.0040.0030.0450.0210.0200.0290.0260.0040.0050.0000.0010.0150.0140.011
Soil_Type40.2390.1770.1380.0500.0350.0950.0480.0780.0550.0870.3420.1330.0340.1380.0220.0110.0170.0131.0000.0080.0160.0010.0020.0060.0360.0220.0340.0260.0040.0000.0100.0110.0080.0120.0190.0050.0360.0490.0290.0040.0100.0060.0060.0730.0350.0320.0470.0430.0080.0080.0010.0030.0240.0230.018
Soil_Type50.2880.0260.0850.0160.0340.0990.0710.0790.0590.0660.1630.0470.0120.0460.2010.0030.0060.0040.0081.0000.0050.0000.0000.0010.0130.0080.0120.0090.0000.0000.0030.0040.0020.0040.0060.0010.0130.0170.0100.0000.0030.0010.0010.0260.0120.0110.0160.0150.0020.0020.0000.0000.0090.0080.006
Soil_Type60.3110.0260.0120.0410.0470.1410.0240.0170.0160.1080.2820.0970.0250.0940.4100.0080.0120.0100.0160.0051.0000.0000.0010.0040.0260.0160.0250.0190.0030.0000.0070.0080.0060.0090.0130.0040.0260.0350.0210.0020.0070.0040.0040.0530.0250.0230.0340.0310.0050.0060.0000.0020.0180.0170.013
Soil_Type70.0200.0070.0200.0190.0080.0540.0110.0060.0110.0820.0130.0150.0030.0120.0030.0000.0000.0000.0010.0000.0001.0000.0000.0000.0030.0010.0030.0010.0000.0000.0000.0000.0000.0000.0000.0000.0030.0040.0020.0000.0000.0000.0000.0060.0030.0020.0040.0030.0000.0000.0000.0000.0010.0010.000
Soil_Type80.0260.0040.0350.0100.0110.0470.0170.0140.0160.0420.0100.0190.0040.0150.0040.0000.0010.0000.0020.0000.0010.0001.0000.0000.0040.0020.0040.0020.0000.0000.0000.0000.0000.0000.0010.0000.0040.0060.0030.0000.0000.0000.0000.0090.0040.0030.0050.0050.0000.0000.0000.0000.0020.0020.001
Soil_Type90.1530.0310.0340.0250.0280.0620.0260.0130.0270.0520.0330.0490.0100.0390.0110.0030.0050.0040.0060.0010.0040.0000.0001.0000.0110.0060.0100.0080.0000.0000.0030.0030.0020.0030.0050.0000.0110.0150.0080.0000.0020.0010.0000.0220.0100.0090.0140.0130.0010.0020.0000.0000.0070.0070.005
Soil_Type100.5140.1590.2470.0780.0670.2040.2560.2500.1330.2090.4710.2200.0570.0070.4850.0180.0280.0220.0360.0130.0260.0030.0040.0111.0000.0360.0570.0430.0080.0000.0170.0190.0140.0200.0310.0090.0600.0810.0480.0070.0160.0100.0100.1210.0570.0520.0770.0710.0130.0140.0030.0050.0400.0380.030
Soil_Type110.2180.0780.0710.0230.0300.1070.0570.0740.0860.0780.1090.1330.0340.1540.0090.0110.0170.0130.0220.0080.0160.0010.0020.0060.0361.0000.0340.0260.0040.0000.0100.0110.0080.0120.0190.0050.0360.0490.0290.0040.0100.0060.0060.0730.0350.0320.0470.0430.0080.0080.0010.0030.0240.0230.018
Soil_Type120.2780.0940.1750.0760.0710.0990.1190.0810.1390.2960.1980.2580.0540.2050.0610.0170.0270.0210.0340.0120.0250.0030.0040.0100.0570.0341.0000.0410.0070.0000.0160.0180.0130.0190.0300.0090.0580.0770.0450.0060.0150.0100.0090.1160.0550.0500.0730.0680.0120.0130.0030.0050.0390.0360.029
Soil_Type130.1490.1730.2010.0190.0870.1200.1240.0840.2030.1110.1380.1590.0290.1950.0460.0130.0200.0160.0260.0090.0190.0010.0020.0080.0430.0260.0411.0000.0050.0000.0120.0130.0100.0150.0220.0060.0430.0580.0340.0050.0120.0070.0070.0870.0410.0380.0550.0510.0090.0100.0020.0040.0290.0270.022
Soil_Type140.1290.0130.0010.0460.0220.0450.0120.0000.0090.0510.1570.0290.0070.0020.0700.0010.0030.0020.0040.0000.0030.0000.0000.0000.0080.0040.0070.0051.0000.0000.0010.0020.0000.0020.0040.0000.0080.0110.0060.0000.0010.0000.0000.0160.0070.0070.0100.0090.0000.0000.0000.0000.0050.0050.004
Soil_Type150.0160.0040.0000.0000.0000.0040.0000.0010.0000.0040.0130.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Soil_Type160.0700.0190.0370.0760.0470.0550.0250.0180.0360.1030.0370.0430.0030.0450.0080.0050.0080.0060.0100.0030.0070.0000.0000.0030.0170.0100.0160.0120.0010.0001.0000.0050.0040.0060.0090.0020.0170.0230.0140.0010.0040.0020.0020.0350.0160.0150.0220.0200.0030.0040.0000.0000.0110.0110.008
Soil_Type170.1320.0180.0440.0920.0490.0590.0250.0300.0380.0400.1930.0690.0180.0520.0530.0050.0090.0070.0110.0040.0080.0000.0000.0030.0190.0110.0180.0130.0020.0000.0051.0000.0040.0060.0100.0020.0190.0260.0150.0010.0050.0030.0030.0380.0180.0160.0240.0220.0040.0040.0000.0000.0130.0120.009
Soil_Type180.1900.0460.0480.0200.0390.0730.0350.0220.0500.1570.0590.0590.0030.0500.0150.0040.0060.0050.0080.0020.0060.0000.0000.0020.0140.0080.0130.0100.0000.0000.0040.0041.0000.0040.0070.0010.0140.0190.0110.0000.0030.0020.0010.0280.0130.0120.0180.0170.0020.0030.0000.0000.0090.0090.007
Soil_Type190.0680.0110.1060.0560.0510.0920.0540.0490.0580.0250.0480.0390.0370.0450.0220.0060.0090.0070.0120.0040.0090.0000.0000.0030.0200.0120.0190.0150.0020.0000.0060.0060.0041.0000.0100.0030.0210.0280.0160.0020.0050.0030.0030.0410.0190.0180.0260.0240.0040.0040.0000.0010.0140.0130.010
Soil_Type200.0880.0450.0790.1020.0660.0890.0550.0360.0680.1220.0450.0720.0260.0440.0330.0090.0140.0110.0190.0060.0130.0000.0010.0050.0310.0190.0300.0220.0040.0000.0090.0100.0070.0101.0000.0040.0310.0420.0250.0030.0080.0050.0050.0630.0300.0270.0400.0370.0060.0070.0000.0020.0210.0200.016
Soil_Type210.0240.0480.0260.0480.0240.0370.0310.0320.0460.0300.0470.0340.0090.0430.0100.0020.0040.0030.0050.0010.0040.0000.0000.0000.0090.0050.0090.0060.0000.0000.0020.0020.0010.0030.0041.0000.0090.0120.0070.0000.0020.0000.0000.0190.0090.0080.0120.0110.0010.0010.0000.0000.0060.0060.004
Soil_Type220.2240.0420.0570.0570.0760.1330.0410.0390.0460.0540.2120.0690.1220.0920.0640.0180.0280.0230.0360.0130.0260.0030.0040.0110.0600.0360.0580.0430.0080.0000.0170.0190.0140.0210.0310.0091.0000.0820.0480.0070.0160.0110.0100.1230.0580.0530.0780.0720.0130.0140.0030.0050.0410.0380.030
Soil_Type230.1930.0280.2120.1670.1620.0500.1230.1310.1410.0790.1870.0300.1350.0480.0870.0240.0380.0300.0490.0170.0350.0040.0060.0150.0810.0490.0770.0580.0110.0000.0230.0260.0190.0280.0420.0120.0821.0000.0650.0090.0220.0140.0130.1650.0780.0710.1050.0960.0170.0190.0040.0070.0550.0520.041
Soil_Type240.0880.1590.1140.0380.0410.0680.1220.1410.0280.0700.0840.1220.0430.1290.0510.0140.0220.0180.0290.0100.0210.0020.0030.0080.0480.0290.0450.0340.0060.0000.0140.0150.0110.0160.0250.0070.0480.0651.0000.0050.0130.0080.0080.0970.0460.0420.0610.0570.0100.0110.0020.0040.0320.0300.024
Soil_Type250.0360.0590.0360.0270.1390.0730.0400.0100.0360.0740.0150.0260.1230.0250.0070.0010.0030.0020.0040.0000.0020.0000.0000.0000.0070.0040.0060.0050.0000.0000.0010.0010.0000.0020.0030.0000.0070.0090.0051.0000.0010.0000.0000.0140.0060.0060.0090.0080.0000.0000.0000.0000.0040.0040.003
Soil_Type260.0710.0740.0450.0190.0220.0780.0380.0330.0330.0730.0530.0600.0150.0760.0170.0040.0070.0060.0100.0030.0070.0000.0000.0020.0160.0100.0150.0120.0010.0000.0040.0050.0030.0050.0080.0020.0160.0220.0130.0011.0000.0020.0020.0330.0160.0140.0210.0190.0030.0030.0000.0000.0110.0100.008
Soil_Type270.0410.0670.0510.1110.1170.0500.0470.0240.0550.0440.0200.0390.0100.0490.0110.0030.0050.0040.0060.0010.0040.0000.0000.0010.0100.0060.0100.0070.0000.0000.0020.0030.0020.0030.0050.0000.0110.0140.0080.0000.0021.0000.0000.0210.0100.0090.0140.0120.0010.0020.0000.0000.0070.0060.005
Soil_Type280.0620.0690.0900.0330.1230.0710.1520.0080.1730.0380.0370.0360.0090.0460.0100.0020.0040.0030.0060.0010.0040.0000.0000.0000.0100.0060.0090.0070.0000.0000.0020.0030.0010.0030.0050.0000.0100.0130.0080.0000.0020.0001.0000.0200.0090.0080.0130.0120.0010.0010.0000.0000.0060.0060.005
Soil_Type290.2380.1110.0950.0720.0900.3310.0840.0800.0920.2160.1970.5510.1140.4370.1300.0360.0570.0450.0730.0260.0530.0060.0090.0220.1210.0730.1160.0870.0160.0000.0350.0380.0280.0410.0630.0190.1230.1650.0970.0140.0330.0210.0201.0000.1160.1070.1570.1440.0260.0280.0070.0110.0830.0780.061
Soil_Type300.1230.1320.0810.0540.0240.1210.1380.0430.1320.0740.1480.2590.0540.2060.0610.0170.0270.0210.0350.0120.0250.0030.0040.0100.0570.0350.0550.0410.0070.0000.0160.0180.0130.0190.0300.0090.0580.0780.0460.0060.0160.0100.0090.1161.0000.0500.0740.0680.0120.0130.0030.0050.0390.0360.029
Soil_Type310.0930.0920.0840.0750.0440.1450.0830.0510.0570.0780.0800.1940.0340.2370.0560.0150.0250.0200.0320.0110.0230.0020.0030.0090.0520.0320.0500.0380.0070.0000.0150.0160.0120.0180.0270.0080.0530.0710.0420.0060.0140.0090.0080.1070.0501.0000.0680.0620.0110.0120.0020.0040.0360.0330.027
Soil_Type320.1900.0950.1400.1390.0560.1580.0980.1210.1260.1060.1070.2840.0290.3130.0820.0230.0360.0290.0470.0160.0340.0040.0050.0140.0770.0470.0730.0550.0100.0000.0220.0240.0180.0260.0400.0120.0780.1050.0610.0090.0210.0140.0130.1570.0740.0681.0000.0910.0170.0180.0040.0070.0520.0490.039
Soil_Type330.0980.0660.2310.0990.1660.1360.1000.1290.1270.0910.0940.2620.0140.2940.0760.0210.0330.0260.0430.0150.0310.0030.0050.0130.0710.0430.0680.0510.0090.0000.0200.0220.0170.0240.0370.0110.0720.0960.0570.0080.0190.0120.0120.1440.0680.0620.0911.0000.0150.0160.0040.0060.0480.0450.036
Soil_Type340.0330.0610.0130.1050.0750.0460.0190.0380.0270.0460.0430.0480.0120.0600.0140.0030.0060.0040.0080.0020.0050.0000.0000.0010.0130.0080.0120.0090.0000.0000.0030.0040.0020.0040.0060.0010.0130.0170.0100.0000.0030.0010.0010.0260.0120.0110.0170.0151.0000.0020.0000.0000.0090.0080.006
Soil_Type350.1360.0300.0240.0360.0200.0510.0290.0150.0300.0500.1490.0120.0550.0050.0150.0040.0060.0050.0080.0020.0060.0000.0000.0020.0140.0080.0130.0100.0000.0000.0040.0040.0030.0040.0070.0010.0140.0190.0110.0000.0030.0020.0010.0280.0130.0120.0180.0160.0021.0000.0000.0000.0090.0090.007
Soil_Type360.0400.0370.0150.0670.0220.0330.0090.0160.0130.0210.0380.0130.0030.0160.0030.0000.0000.0000.0010.0000.0000.0000.0000.0000.0030.0010.0030.0020.0000.0000.0000.0000.0000.0000.0000.0000.0030.0040.0020.0000.0000.0000.0000.0070.0030.0020.0040.0040.0000.0001.0000.0000.0020.0010.000
Soil_Type370.0670.0270.0090.0170.0050.0470.0120.0240.0240.0350.1180.0150.0050.0100.0060.0000.0020.0010.0030.0000.0020.0000.0000.0000.0050.0030.0050.0040.0000.0000.0000.0000.0000.0010.0020.0000.0050.0070.0040.0000.0000.0000.0000.0110.0050.0040.0070.0060.0000.0000.0001.0000.0030.0030.002
Soil_Type380.3860.0320.0740.0700.0200.0910.0390.0460.0570.0930.3410.0110.0610.0170.0430.0120.0190.0150.0240.0090.0180.0010.0020.0070.0400.0240.0390.0290.0050.0000.0110.0130.0090.0140.0210.0060.0410.0550.0320.0040.0110.0070.0060.0830.0390.0360.0520.0480.0090.0090.0020.0031.0000.0260.020
Soil_Type390.3130.0460.0960.0430.0540.1110.0700.0720.0870.0440.3310.0130.0110.0020.0410.0110.0180.0140.0230.0080.0170.0010.0020.0070.0380.0230.0360.0270.0050.0000.0110.0120.0090.0130.0200.0060.0380.0520.0300.0040.0100.0060.0060.0780.0360.0330.0490.0450.0080.0090.0010.0030.0261.0000.019
Soil_Type400.6220.0340.0310.1660.2140.0690.0170.0410.0400.0640.2660.0120.1050.0430.0320.0090.0140.0110.0180.0060.0130.0000.0010.0050.0300.0180.0290.0220.0040.0000.0080.0090.0070.0100.0160.0040.0300.0410.0240.0030.0080.0050.0050.0610.0290.0270.0390.0360.0060.0070.0000.0020.0200.0191.000

Missing values

2023-02-02T18:34:24.247888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-02T18:34:25.673879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40target
0259651325805102212321486279100000000000000000000000000000001000000000005
12590562212-63902202351516225100000000000000000000000000000001000000000005
2280413992686531802342381356121100000000000000100000000000000000000000000002
327851551824211830902382381226211100000000000000000000000000000000100000000002
42595452153-13912202341506172100000000000000000000000000000001000000000005
525791326300-15672302371406031100000000000000000000000000000001000000000002
6260645727056332222251386256100000000000000000000000000000001000000000005
7260549423475732222301446228100000000000000000000000000000001000000000005
82617459240566662232211336244100000000000000000000000000000001000000000005
926125910247116362282191246230100000000000000000000000000000001000000000005
ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40target
58100224191682510833124230240126812001001000000000000000000000000000000000000003
5810032415161259529120236237116815001001000000000000000000000000000000000000003
5810042410158249024120238236115819001001000000000000000000000000000000000000003
5810052405159229019120237238119824001001000000000000000000000000000000000000003
5810062401157219015120238238119830001001000000000000000000000000000000000000003
5810072396153208517108240237118837001001000000000000000000000000000000000000003
581008239115219671295240237119845001001000000000000000000000000000000000000003
58100923861591760790236241130854001001000000000000000000000000000000000000003
58101023841701560590230245143864001001000000000000000000000000000000000000003
58101123831651360467231244141875001001000000000000000000000000000000000000003